Molecular mapping of quantitative trait loci in japonica rice

Genome ◽  
1996 ◽  
Vol 39 (2) ◽  
pp. 395-403 ◽  
Author(s):  
Edilberto D. Redoña ◽  
David J. Mackill
Keyword(s):  
Oryza Sativa ◽  
Quantitative Trait Loci ◽  
Qtl Mapping ◽  
Phenotypic Variation ◽  
Quantitative Trait ◽  
Rflp Markers ◽  
Seedling Vigor ◽  
Trait Loci ◽  
Rice Chromosomes ◽  
Molecular Maps

Rice (Oryza sativa L.) molecular maps have previously been constructed using interspecific crosses or crosses between the two major subspecies: indica and japonica. For japonica breeding programs, however, it would be more suitable to use intrasubspecific crosses. A linkage map of 129 random amplified polymorphic DNA (RAPD) and 18 restriction fragment length polymorphism (RFLP) markers was developed using 118 F2 plants derived from a cross between two japonica cultivars with high and low seedling vigor, Italica Livorno (IL) and Labelle (LBL), respectively. The map spanned 980.5 cM (Kosambi function) with markers on all 12 rice chromosomes and an average distance of 7.6 cM between markers. Codominant (RFLP) and coupling phase linkages (among RAPDs) accounted for 79% of total map length and 71% of all intervals. This map contained a greater percentage of markers on chromosome 10, the least marked of the 12 rice chromosomes, than other rice molecular maps, but had relatively fewer markers on chromosomes 1 and 2. We used this map to detect quantitative trait loci (QTL) for four seedling vigor related traits scored on 113 F3 families in a growth chamber slantboard test at 18 °C. Two coleoptile, five root, and five mesocotyl length QTLs, each accounting for 9–50% of the phenotypic variation, were identified by interval analysis. Single-point analysis confirmed interval mapping results and detected additional markers significantly influencing each trait. About two-thirds of alleles positive for the putative QTLs were from the high-vigor parent, IL. One RAPD marker (OPAD13720) was associated with a IL allele that accounted for 18.5% of the phenotypic variation for shoot length, the most important determinant of seedling vigor in water-seeded rice. Results indicate that RAPDs are useful for map development and QTL mapping in rice populations with narrow genetic base, such as those derived from crosses among japonica cultivars. Other potential uses of the map are discussed. Key words : QTL mapping, RAPD, RFLP, seedling vigor, japonica, Oryza sativa.

Mapping of quantitative trait loci controlling seedling vigor in rice (Oryza sativa L.) under submergence

Euphytica ◽  
2012 ◽  
Vol 192 (1) ◽  
pp. 63-75 ◽  
Author(s):  
O. E. Manangkil ◽  
H. T. T. Vu ◽  
N. Mori ◽  
S. Yoshida ◽  
C. Nakamura
Keyword(s):  
Oryza Sativa ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Oryza Sativa L ◽  
Seedling Vigor ◽  
Trait Loci

scholarly journals Genetic Mapping of Quantitative Trait Loci Controlling Growth and Wood Quality Traits in Eucalyptus grandis Using a Maternal Half-sib Family and RAPD Markers

Genetics ◽  
1996 ◽  
Vol 144 (3) ◽  
pp. 1205-1214 ◽  
Author(s):  
Dario Grattapaglia ◽  
Fernando L G Bertolucci ◽  
Ricardo Penchel ◽  
Ronald R Sederoff
Keyword(s):  
Genetic Variation ◽  
Quantitative Trait Loci ◽  
Qtl Mapping ◽  
Phenotypic Variation ◽  
Quantitative Trait ◽  
Wood Quality ◽  
Volume Growth ◽  
Eucalyptus Grandis ◽  
Forest Productivity ◽  
Trait Loci

Abstract Quantitative trait loci (QTL) mapping of forest productivity traits was performed using an open pollinated half-sib family of Eucalyptus grandis. For volume growth, a sequential QTL mapping approach was applied using bulk segregant analysis (BSA), selective genotyping (SG) and cosegregation analysis (CSA). Despite the low heritability of this trait and the heterogeneous genetic background employed for mapping, BSA detected one putative QTL and SG two out of the three later found by CSA. The three putative QTL for volume growth were found to control 13.7% of the phenotypic variation, corresponding to an estimated 43.7% of the genetic variation. For wood specific gravity five QTL were identified controlling 24.7% of the phenotypic variation corresponding to 49% of the genetic variation. Overlapping QTL for CBH, WSG and percentage dry weight of bark were observed. A significant case of digenic epistasis was found, involving unlinked QTL for volume. Our results demonstrate the applicability of the within half-sib design for QTL mapping in forest trees and indicate the existence of major genes involved in the expression of economically important traits related to forest productivity in Eucalyptus grandis. These findings have important implications for marker-assisted tree breeding.

scholarly journals On the Differences Between Maximum Likelihood and Regression Interval Mapping in the Analysis of Quantitative Trait Loci

Genetics ◽  
2000 ◽  
Vol 156 (2) ◽  
pp. 855-865 ◽  
Author(s):  
Chen-Hung Kao
Keyword(s):  
Quantitative Trait Loci ◽  
Maximum Likelihood ◽  
Qtl Mapping ◽  
Quantitative Trait ◽  
Mean Squared Error ◽  
Interval Mapping ◽  
Size Difference ◽  
Ratio Test ◽  
Trait Loci ◽  
Variance Explained

AbstractThe differences between maximum-likelihood (ML) and regression (REG) interval mapping in the analysis of quantitative trait loci (QTL) are investigated analytically and numerically by simulation. The analytical investigation is based on the comparison of the solution sets of the ML and REG methods in the estimation of QTL parameters. Their differences are found to relate to the similarity between the conditional posterior and conditional probabilities of QTL genotypes and depend on several factors, such as the proportion of variance explained by QTL, relative QTL position in an interval, interval size, difference between the sizes of QTL, epistasis, and linkage between QTL. The differences in mean squared error (MSE) of the estimates, likelihood-ratio test (LRT) statistics in testing parameters, and power of QTL detection between the two methods become larger as (1) the proportion of variance explained by QTL becomes higher, (2) the QTL locations are positioned toward the middle of intervals, (3) the QTL are located in wider marker intervals, (4) epistasis between QTL is stronger, (5) the difference between QTL effects becomes larger, and (6) the positions of QTL get closer in QTL mapping. The REG method is biased in the estimation of the proportion of variance explained by QTL, and it may have a serious problem in detecting closely linked QTL when compared to the ML method. In general, the differences between the two methods may be minor, but can be significant when QTL interact or are closely linked. The ML method tends to be more powerful and to give estimates with smaller MSEs and larger LRT statistics. This implies that ML interval mapping can be more accurate, precise, and powerful than REG interval mapping. The REG method is faster in computation, especially when the number of QTL considered in the model is large. Recognizing the factors affecting the differences between REG and ML interval mapping can help an efficient strategy, using both methods in QTL mapping to be outlined.

scholarly journals Multiple Cold Tolerance Trait Phenotyping Reveals Shared Quantitative Trait Loci in Oryza sativa

Rice ◽  
2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Naoki Shimoyama ◽  
Melineeh Johnson ◽  
André Beaumont ◽  
Michael Schläppi
Keyword(s):  
Oryza Sativa ◽  
Quantitative Trait Loci ◽  
Cold Tolerance ◽  
Quantitative Trait ◽  
Trait Loci

scholarly journals Quantitative Trait Loci Affecting Flag Leaf Development in Rice (Oryza sativa L.)

2003 ◽  
Vol 53 (3) ◽  
pp. 255-262 ◽  
Author(s):  
Sohei Kobayashi ◽  
Yoshimichi Fukuta ◽  
Satoshi Morita ◽  
Tadashi Sato ◽  
Mitsuru Osaki ◽  
...  
Keyword(s):  
Oryza Sativa ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Leaf Development ◽  
Oryza Sativa L ◽  
Flag Leaf ◽  
Trait Loci

Mapping of quantitative trait loci for gibberellic acid response at rice (Oryza sativa L.) seedling stage

Plant Science ◽  
2006 ◽  
Vol 170 (1) ◽  
pp. 12-17 ◽  
Author(s):  
Yanjun Dong ◽  
H. Kamiuten ◽  
Zhongnan Yang ◽  
Dongzhi Lin ◽  
T. Ogawa ◽  
...  
Keyword(s):  
Oryza Sativa ◽  
Quantitative Trait Loci ◽  
Gibberellic Acid ◽  
Quantitative Trait ◽  
Oryza Sativa L ◽  
Seedling Stage ◽  
Trait Loci

Identification of quantitative trait loci underlying lutein content in soybean seeds across multiple environments

2017 ◽  
Vol 155 (8) ◽  
pp. 1263-1271 ◽  
Author(s):  
W. L. TENG ◽  
W. J. FENG ◽  
J. Y. ZHANG ◽  
N. XIA ◽  
J. GUO ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Phenotypic Variation ◽  
Quantitative Trait ◽  
Average Distance ◽  
Recombinant Inbred Lines ◽  
Chromosome 9 ◽  
Soybean Seeds ◽  
Trait Loci ◽  
Breeding Programmes ◽  
Lutein Content

SUMMARYLutein benefits human health significantly, including that of the eyes, skin and heart. Therefore, increasing lutein content in soybean seeds is an important objective for breeding programmes. However, no information about soybean lutein-related quantitative trait loci (QTL) has been reported, as of 2016. The aim of the present study was to identify QTLs underlying the lutein content in soybean seeds. A population including 129 recombinant inbred lines was developed from the cross between ‘Dongnong46’ (lutein 13·10 µg/g) and ‘L-100’ (lutein 23·96 µg/g), which significantly differed in seed lutein contents. This population was grown in ten environments including Harbin in 2012, 2013, 2014 and 2015; Hulan in 2013, 2014 and 2015; and Acheng in 2013, 2014 and 2015. A total of 213 simple sequence repeat markers were used to construct the genetic linkage map, which covered approximately 3623·39 cM, with an average distance of 17·01 cM between markers. In the present study, eight QTLs associated with lutein content were found initially, which could explain 1·01–19·66% of the observed phenotypic variation in ten different tested environments. The phenotypic contribution of qLU-1 (located near BARC-Satt588 on chromosome 9 (Chr 9; linkage group (LG) K)) was >10% across seven tested environments, while qLU-2 (located near Satt192 of Chr 12 (LG H)) and qLU-3 (located near Satt353 of Chr12 (LGH)) could explain 5–10% of the observed phenotypic variation in more than seven environments, respectively. qLU-5, qLU-6, qLU-7 and qLU-8 could be detected in more than four environments. These eight QTLs were novel, and have considerable potential value for marker-assistant selection of higher lutein content in soybean lines.

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